wolffd@0: function n2 = mdndist2(mixparams, t)
wolffd@0: %MDNDIST2 Calculates squared distance between centres of Gaussian kernels and data
wolffd@0: %
wolffd@0: %	Description
wolffd@0: %	N2 = MDNDIST2(MIXPARAMS, T) takes takes the centres of the Gaussian
wolffd@0: %	contained in  MIXPARAMS and the target data matrix, T, and computes
wolffd@0: %	the squared  Euclidean distance between them.  If T has M rows and N
wolffd@0: %	columns, then the CENTRES field in the MIXPARAMS structure should
wolffd@0: %	have M rows and N*MIXPARAMS.NCENTRES columns: the centres in each row
wolffd@0: %	relate to the corresponding row in T. The result has M rows and
wolffd@0: %	MIXPARAMS.NCENTRES columns. The I, Jth entry is the  squared distance
wolffd@0: %	from the Ith row of X to the Jth centre in the Ith row of
wolffd@0: %	MIXPARAMS.CENTRES.
wolffd@0: %
wolffd@0: %	See also
wolffd@0: %	MDNFWD, MDNPROB
wolffd@0: %
wolffd@0: 
wolffd@0: %	Copyright (c) Ian T Nabney (1996-2001)
wolffd@0: %	David J Evans (1998)
wolffd@0: 
wolffd@0: % Check arguments for consistency
wolffd@0: errstring = consist(mixparams, 'mdnmixes');
wolffd@0: if ~isempty(errstring)
wolffd@0:   error(errstring);
wolffd@0: end
wolffd@0: 
wolffd@0: ncentres   = mixparams.ncentres;
wolffd@0: dim_target = mixparams.dim_target;
wolffd@0: ntarget    = size(t, 1);
wolffd@0: if ntarget ~= size(mixparams.centres, 1)
wolffd@0:   error('Number of targets does not match number of mixtures')
wolffd@0: end
wolffd@0: if size(t, 2) ~= mixparams.dim_target
wolffd@0:   error('Target dimension does not match mixture dimension')
wolffd@0: end
wolffd@0: 
wolffd@0: % Build t that suits parameters, that is repeat t for each centre
wolffd@0: t = kron(ones(1, ncentres), t);
wolffd@0: 
wolffd@0: % Do subtraction and square
wolffd@0: diff2 = (t - mixparams.centres).^2;
wolffd@0: 
wolffd@0: % Reshape and sum each component
wolffd@0: diff2 = reshape(diff2', dim_target, (ntarget*ncentres))';
wolffd@0: n2 = sum(diff2, 2);
wolffd@0: 
wolffd@0: % Calculate the sum of distance, and reshape
wolffd@0: % so that we have a distance for each centre per target
wolffd@0: n2 = reshape(n2, ncentres, ntarget)';
wolffd@0: